texto = open('/...evaluate_model.log', 'r') #write the location of the evaluate_model.log that comes from evaluate_model.py
lineas = texto.readlines()
texto.close()

tupleses = []
nline = []
for i in lineas:
    i = i.replace(" ","")
    i = i.replace("/n","")
    i = i.strip()
    word = i.split(' ')

    if "DOPEscore:" in i:
        i = i.replace("DOPEscore:","")
        if "$" in i:
            pass
        else:
            nline.append(i)
    if "openf___224_>Open" in i:
        i = i.replace("openf___224_>Open","")
        if "$" in i:
            pass
        else:
            nline.append(i)

    
    
    

for n in nline:

    try:
        try:
            m = float(n)
            tuplese = nline[nline.index(n)+1], m
            
            tupleses.append(tuplese)
        except ValueError:
            pass
    except IndexError:
        pass

       
pnames = []
tupleses_b=[]

for x in tupleses:
    nombre = x[0].split(".")
    pnames.append(nombre[0])
    cuc = (nombre[0], x)
    tupleses_b.append(cuc)
    



pnames = set(pnames)


for n in pnames:
    valueses = []
    preyses = []
    for i in tupleses_b:
        if n == i[0]:
            valueses.append(i[1][1])
            preyses.append(i[1][0])
    minim = min(valueses)
    corr = preyses[valueses.index(minim)], valueses[valueses.index(minim)]
    texto = open("...BEST_STRUCTURES.txt", "a") #write the output directory for the list with the best structures for each initial .ali sequence
    texto.write(str(corr))
    texto.write("\n")
    texto.close()
               
            
            
    
